Association of the glucose patterns after a single nonstandardized meal with the habitual diet composition and features of the daily glucose profile in individuals without diabetes

Abstract

Background: The postprandial glucose response (PPGR), contributing to the glycemic variability (GV), is positively associated with cardiovascular disease risk in people without diabetes, and can thus represent a target for cardiometabolic prevention strategies.

Objectives: The study aimed to distinguish patterns of PPGR after a single nonstandardized meal and to evaluate their relationship with the habitual diet and the daily glucose profile (DGP) in individuals at high-cardiometabolic risk.

Methods: Baseline 4-d continuous glucose monitoring was performed in 159 adults recruited in the MEDGI-Carb trial. After a nonstandardized breakfast, parameters of the PPGR were estimated by a mechanistic model: baseline glucose; amplitude-the magnitude of postmeal glucose concentrations; frequency-the velocity of postmeal glucose oscillations; damping-the rate of postmeal glucose decay. PPGR patterns were identified by cluster analysis. Differences between clusters and the relationship between PPGR parameters and individual features were explored by one-way analysis of variance and correlation analysis, respectively.

Results: Two patterns of PPGR emerged. Pattern A had a higher baseline, amplitude, frequency, and damping than B. Individuals in cluster A compared with B had higher energy (2002 ± 526 compared with 1766 ± 455 kcal, P = 0.025), protein (82 ± 22 compared with 72 ± 21 g, P = 0.028), and fat (87 ± 30 compared with 75 ± 22 g, P = 0.041), but not carbohydrate habitual intake. Pattern A compared to B associated with a higher average daily glucose (6.12 ± 0.50 compared with 5.88 ± 0.62 mmol/L, P = 0.019) and lower GV (11.67 ± 3.52 compared with 13.43 ± 3.78%, P = 0.010). Mean daily glucose correlated directly with baseline (r_s = 0.419, P < 0.001) and amplitude (r_s = 0.189, P = 0.022) of the PPGR, whereas DGP variability correlated directly with amplitude (r_s = 0.218, P = 0.008), and inversely with frequency (r_s = -0.179, P = 0.031) and damping (r_s = -0.309, P < 0.001).

Conclusions: Two PPGR patterns after a single nonstandardized breakfast were identified in high-cardiometabolic risk individuals. The habitual diet was associated with the patterns and their dynamic parameters, which, in turn, could predict the individuals' DGP. Our findings could support the implementation of dietary strategies targeting the PPGR to ameliorate the cardiometabolic risk profile.

Trial registration number: This study was registered at clinicaltrials.gov as NCT03410719.

Keywords: CGM metrics; cardiometabolic risk; clustering; continuous glucose monitoring; diet; free-living; glucose dynamic; glycemic variability; mechanistic model; postprandial glucose response; precision nutrition.